WO2020231590A1 - Système infonuagique de données de soins de santé, serveur et procédé - Google Patents

Système infonuagique de données de soins de santé, serveur et procédé Download PDF

Info

Publication number
WO2020231590A1
WO2020231590A1 PCT/US2020/028274 US2020028274W WO2020231590A1 WO 2020231590 A1 WO2020231590 A1 WO 2020231590A1 US 2020028274 W US2020028274 W US 2020028274W WO 2020231590 A1 WO2020231590 A1 WO 2020231590A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
utility
mastering
identifiers
identification
Prior art date
Application number
PCT/US2020/028274
Other languages
English (en)
Inventor
Blayne Lequeux
Original Assignee
Blayne Lequeux
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Blayne Lequeux filed Critical Blayne Lequeux
Publication of WO2020231590A1 publication Critical patent/WO2020231590A1/fr

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • G06F16/2272—Management thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof

Definitions

  • NPI National Provider Identifier
  • the average health plan member creates about a dozen claims per year and each claim is reviewed by dozens of plan functions and ancillary organizations with a financial interest in the claim. Each plan member’s claims are reviewed 3 to 5 times creating approximately billions of identification events per year. Even if all identifiers were accurately recognized the overhead burden is significant. If an identification error occurs anywhere in the adjudication process then the claim is pended for review, identification and reprocessing. It is common in healthcare for bills to be sent multiple times due to processing lag, identification errors, reprocessing and late payment cycles.
  • Some embodiments of the Healthcare Data Cloud System, Server and Method includes systems and methods of identifying individuals, patients, employees, entities, corporations, products, structures, landmarks, computer programs, and/or anything that can be identified by a proper name (hereafter an/the“individual” and/or “individuals”).
  • the system identifies individuals by collecting, storing, analyzing, processing, and publishing multiple variations of identifying information associated with an individual.
  • the system associates one or more of those variations with the distributed systems’ records.
  • the system includes a cloud-based reference service provided through a computer-to-computer function called a web service that automatically reviews and uniquely identifies all parties to a healthcare transaction or claim.
  • this Web service is callable by any Web-connected administrative system using a function that submits one or more transactions which are identified, and correct identifiers and verified names and demographic data are appended to each record in milliseconds per record.
  • a record if a record cannot be automatically identified, it is pended, and a reviewer resolves and releases the record to the client.
  • identities may be manually researched by an individual in a Web browser.
  • the system includes Service Offerings.
  • Comprehensive Data-as-a-Service (DaaS) Web services are configured for accepting a single entry or batch file to find a person, organization, or healthcare provider (professional or institution) and return a fixed format response.
  • the system includes Basic and smart DaaS APIs configured to help customers answer a variety of commercial questions.
  • the system includes a Learning Database.
  • a Learning Database retains variations of identifiers (e.g., individuals, organizations, relationships, and/or addresses).
  • the system includes a learning database technology that records variations of a name of an individual, entity, organization, address and other defining attributes.
  • the system is configured to store variable data in a proprietary variations database.
  • a variations database e.g., a variations table
  • a logic system and rules tables is used for identification and managed by the system via a logic system and rules tables.
  • individuals, organizations and addresses that cannot be automatically identified may be resolved through other data sources or manually.
  • the system is configured such that the frequency of names matches for master and errata names are recorded and the source data of each change is recorded along with the date and time.
  • Unique IDs are stored for each address for an individual and organization along with the data source and date.
  • the rules match logic will be enhanced using system logic along with matching the individual person’s identity to the professional provider identity.
  • the system includes a web portal that offers a single-line entry to identify people, organizations, and healthcare institutions and providers by defining parameters such as geography, people demographics, etc.
  • the system is configured to allow users to design and produce reports and perform analyses incorporating statistics, sorting, artificial intelligence and graphic mapping displays.
  • the system s AI and or machine learning is configured to learn through iterative use.
  • iterative use of the service enhances the system because the source data and logic for every edit is retained.
  • the web user interface is configured to guide the user through a process of finding information, answering questions, and adjusting the user interaction based on the experience history of the user.
  • the system is configured for source data maintenance using web services and automated, secure FTP sites to intake, process, edit, refine and load into the system to create the output of clean client data.
  • the system includes one or more databases with data about people, organizations, locations, identifiers, demographics, and attributes.
  • the system includes a population database uniquely identifies millions (e.g., 285 million-plus) of individual people using multiple attributes (e.g., 400-plus attributes).
  • the system includes an organization database that uniquely identifies public, private, social, industry and governmental organizations (e.g., 20 million-plus agencies) with attributes (e.g., 300-plus attributes) and tracks related individuals.
  • the system includes an address database for addresses, misspellings, Lat and Lon, and other geographic definitions (e.g., for maintaining the 150 million-plus addresses in the USA).
  • the system uses one or more of uniquely identifying information such as one or more current and/or past names, addresses, driver’s license numbers, social security number, telephone numbers, pictures, computer readable code, fingerprints, retinal scans, biometric data, metadata (e.g., digital footprints such as driving patterns, purchase patterns, web browser history, etc.), public and/or private records, and/or any other type of identifier associated with an individual to confirm the individual’s identity.
  • name variations can include nicknames, surnames, titles, given names, family names, aliases, user names, corporate names and/or any title an individual may use for identification purposes.
  • Uniquely identifying information and the variation thereof are collectively referred to as an identifier(s), and/or an ID(s) herein.
  • the system is used by healthcare industries. In some embodiments, the system allows for different healthcare systems to communicate information about individuals. In some embodiments, different healthcare systems identify patients using different IDs. In some embodiments, the system creates a database including all the different IDs that has been associated with an individual. In some embodiments, advanced scouring tools are used to gather IDs from multiple online systems to create a database of IDs associated with an individual. In some embodiments, the system is shared by healthcare agencies, law enforcement agencies, government agencies, marketing agencies, and/or any individual, organization, or corporation (collectively referred to as an“agency” and/or“agencies”).
  • agencies use one or more identifiers to associate one or more documents, records, links, and/or data (collectively referred to as data) with a single individual.
  • the system is used by one or more agencies to identify an individual associated with multiple IDs.
  • the system is configured for general use anywhere in the $4 trillion U.S. healthcare system.
  • the system is configured for tracking and identifying individuals during an epidemic to track the source and/or positively identify specific individuals who may have come into contact with each other.
  • the system is configured to compare records (e.g., credit card usage; phone records; entry logs; metadata) from one individual’s ID to another individual’s ID such that infected individuals can be tracked and/or notified.
  • records e.g., credit card usage; phone records; entry logs; metadata
  • the system if fully implemented by agencies would have helped mitigate the effects of the Corona Virus outbreak of 2020, for example.
  • Some embodiments include a system, server and method comprising at least one processor, and at least one non-transitory computer-readable storage medium in data communication with the at least one processor that is configured to store and exchange data comprising or representing data derived or received from at least one server of at least one data source, database, and/or at least one user.
  • Some embodiments include an application programming interface (API) in data communication with the at least one processor and the at least one non-transitory computer-readable storage medium.
  • the application programming interface includes steps executable by the at least one processor to upload, download, or enable access of the content data derived or received from the at least one server of at least one healthcare data source and/or at least one user.
  • one or more of: outpatient, inpatient, prescription, laboratory, dental and vision claims are retained in a cloud system.
  • the outpatient, inpatient, prescription, laboratory, dental and vision claims can be linked to patients, providers and health plans in a manner that facilitates one or more of the following: near real-time, bi directional updating of data sources with a healthcare data cloud system master database; access to interactive web browsers for real-time and batch processes for queries, reports, analysis and research purposes; individuals making inquiries for claims, eligibility, health profiles, benefits, electronic medical records, questions and answers, finding in-network doctors, labs, outpatient facilities, pharmacies, and the like; analysis of provider networks, provider assessment, network optimization; and actuarial underwriting, claims modeling, analysis of group plans and loss-ratio projections.
  • communication with the system occurs interactively through a web browser.
  • the system can use a natural language interface.
  • the natural-language interface allows a user to communicate with the system using common linguistic sentences, phrases, questions, and/or clauses to select, modify, and/or create data.
  • the healthcare data cloud system uses automated web services for computer-to-computer transactions.
  • automated web services allow software that may have different programming languages to communicate over a network (e.g., the World Wide Web).
  • APIs application programming interfaces
  • the system can use an automated and interactive file transfer protocol website.
  • one or more interactive file transfer protocol websites enable file uploads and downloads.
  • the system includes a Natural Language Processing System (NLPS).
  • the system includes a Natural Language Variations Table (NLVT).
  • NLVT Natural Language Variations Table
  • a Web page for user input and requests consists of a single line entry using natural language to query the system, produce reports, load and edit data and numerous other user functions.
  • the NLPS is focused on healthcare applications and terminology referring to medical diagnoses and procedures and terms applying to healthcare claims for outpatient and inpatient services. In some embodiments, if the user uses an unrecognized term the system will ahempt to relate the entry to similar terms in the NLVT.
  • the system if relating the entry to similar terms in the NLVT is successful the system is configured to store the term for the user. In some embodiments, if relating the entry to similar terms in the NLVT is unsuccessful, the system is configured to enable manual entry of the term (e.g., the term is researched by a technician and new terms are added to the system).
  • the NLPS includes all data definitions and variations from the DIMU and DMU then into the NLVT. Reports and terms created in the RDMU are also incorporated into the NLVT and can be reference in a natural language user request. Individual names and organizations entered through the NLPS are recognized using the functions of the IMU and the related variations tables.
  • the systems and data sets are designed to incorporate deep learning and artificial intelligence.
  • systems and data sets can give fast access and analysis of vast amounts of data.
  • systems and data sets can improve healthcare and healthcare costs.
  • some embodiments of the healthcare data cloud system are configured to respond to questions.
  • example input questions and system responses are as follows:
  • o System response The person contracted a virus that damages the lungs and creates a fluid discharge that fills the lungs.
  • Drugs should be administered to kill the virus and all people exposed to the patient should be given an antiviral drug blocking the virus.
  • FIG. 1 illustrates a flow chart of a hybrid cloud system according to some embodiments.
  • FIG. 2 illustrates a flow chart of the system’s operations according to some embodiments.
  • FIG. 3 illustrates a flow chart of Population, Organizations, Addresses and Healthcare Tables according to some embodiments.
  • FIG. 4 illustrates an example service market the system is configured to support according to some embodiments.
  • FIG. 5 illustrates a computer server system network in communication with the system according to some embodiments.
  • FIG. 6 illustrates another flow chart of the system’s operations and components according to some embodiments.
  • FIG. 1 illustrates a flow chart of a hybrid cloud system according to some embodiments.
  • the Healthcare Data Cloud System (HDC; the system) operates in a hybrid cloud environment.
  • the healthcare data cloud system’s application can scale efficiently for rapid growth.
  • the system uses conventional cloud services (e.g., Microsoft Azure, Amazon Web Services, etc.)
  • hybrid cloud architecture is designed to incorporate external computers, disk arrays and other cloud environments.
  • the system can access large troves of healthcare data without migrating to the cloud until usage volume requires a move to the cloud.
  • FIG. 2 illustrates a flow chart of the system’s operations and components according to some embodiments.
  • the system includes an Identification Mastering Utility (IMU); a Data Mastering Utility (DMU); a Report Designer Management Utility (RDMU); and a Data Interface Designer and Management Utility (DIDMU).
  • IMU Identification Mastering Utility
  • DMU Data Mastering Utility
  • RDMU Report Designer Management Utility
  • DIDMU Data Interface Designer and Management Utility
  • the arrows represent a bi-directional flow of data between each utility.
  • the Identification Mastering Utility includes tables that store multiple ID variations.
  • the IMU uses different tables for different ID types.
  • different ID types are stored in at least one of a Population Table, an Organization Table, and Address Table, and/or a Variations Table.
  • the system accesses one or more tables, utilities, and or modules described herein intermittently, consecutively, and/or simultaneously (simultaneously as used herein can include lag and or latency times associated with a conventional computer attempting to process multiple types of data at the same time).
  • the system includes a Population Table.
  • the Population Table includes data on individuals in a geographical area.
  • a Population Table includes data on individuals in a country and/or in multiple countries.
  • the geographical area is used as an identifier.
  • the system includes an Organization Table.
  • the Organization Table links an individual’s data to an agency.
  • the Organization Table links an individual’s data to multiple agencies.
  • agencies are used as an identifier.
  • the system includes an Address Table.
  • the Address Table links an individual’s data to an address.
  • the Address Table links an individual’s data to multiple addresses.
  • addresses are used as a unique identifier.
  • the system includes one or more Variations Tables (a reference to a single Variations Table and/or multiple Variations Tables are collectively referred to as a/the Variations Table herein; a reference to a“table” is a refence to a table other than the Variations Table unless stated otherwise; a reference to a“table” may include any table that is part of the system and/or located in a separate database as described herein).
  • the Variations Table includes data variations from or more other tables.
  • each table has a corresponding Variations Table.
  • data variation from multiple tables are stored in a single Variations Table.
  • multiple Variations Tables include data from a single table.
  • a Variations Table includes any ID variations associated with an individual.
  • “table” includes any conventional data presentation format.
  • example ID variations including name spellings (including misspellings), addresses, phone numbers or any ID describing an individual, and/or the error associated with the IDs.
  • Table 1 shows a Variations Table including variations of and ID for an individual Mary Jane Smith Jones, MD, according to some embodiments.
  • each numbered row corresponds to an ID used by one or more organizations.
  • the highlighted ID in row 1 is a master ID.
  • all IDs bolded and underlined in the Variations Table refer to the same individual: most ID variations have correct spellings but the underlined ID variations are errors.
  • the IMU accumulates variations in IDs (e.g., names, addresses, titles, degrees, phone numbers, labels, personal attributes, organizational relationships and any other data elements from one or more agencies) and stores them in the Variations Table according to some embodiments.
  • IDs e.g., names, addresses, titles, degrees, phone numbers, labels, personal attributes, organizational relationships and any other data elements from one or more agencies
  • the ID variations are ranked and labeled by frequency, accuracy, date entered, and ID currently used.
  • the highest ranked ID is labeled as a master ID.
  • one or more records associated with each ID variation is also associated with the master ID.
  • entry of any ID variation from the Variations Table causes the system to form links to data associated with the master ID (e.g., hospital records, criminal records, etc).
  • each ID includes one or more data elements.
  • example data elements are shown in Table 3.
  • the IMU includes a Name Parsing Module (NPM).
  • NPM Name Parsing Module
  • the NPM parses an ID from rows in one or more tables (e.g., the Variations Table) into one or more columns using an ID vector (IDV; also called a name vector).
  • IDV also called a name vector
  • ID vector instead of a name being stored in data fields such as First, Middle, Last names, names are stored in a name vector where names, spaces, and hyphens associated with an ID are stored with notation of the order.
  • matching logic is applied to the vector comparing it to all similar names in the Variations table and finding all possible matches.
  • additional data is then used to resolve the matches to one or a few choices.
  • the ID vector adds common and/or defined ID variations (e.g., names, spaces, hyphens, surname order, language-specific spellings, known misspellings, and/or punctuations) automatically to each ID and/or data element entered into the system as an ID iteration in one or more Variations Tables (e.g., a row in Table 1) and/or other tables.
  • common and/or defined ID variations e.g., names, spaces, hyphens, surname order, language-specific spellings, known misspellings, and/or punctuations
  • the system is configured to apply name match logic.
  • name match logic includes the application of a set a set of rules that utilizes the variations tables for individual names and organizations, addresses and IDs.
  • the logic table is derived by utilizing artificial intelligence routines to create 300 plus rules that utilize the tables which creates logical choices used to match names, organizations, addresses, claims, medical reports, etc.
  • NPM accesses one or more tables and applies the NMS steps described below.
  • a primary name shown is Mary Jane Smith Jones.
  • the name Mary Jane Smith Jones could appear in the Variations Table in any combination according to some embodiments.
  • the NPM can loop through the Name Match Sequence several times and apply some or all combination of IDs a and/or data elements in each row to get a table that includes columns representing each ID a and/or data element component (e.g., first name, last name, address, etc).
  • the system uses artificial intelligence (AI) to determine each ID and/or data element component data type.
  • each ID and/or data element component type is listed under a different field.
  • the system includes a Data Editing, Proper Casing and Enrichment Module (DEPCEM) configured to be used by the IDM.
  • DEPCEM is configured to create and incorporate edit tables.
  • the DEPCEM is configured to standardize data so that identification matches and statistical analyses function properly.
  • the DEPCEM includes tables such as titles, degrees and suffix (Jr, II, III, IV), Scottish names, proper casing and the like, ensure a standard approach to spelling.
  • the DEPCEM includes tables that include abbreviations such as degrees (i.e., PhD, MA, DO, MD), (Jr, III, IV), numbers and math symbols, proper formats for currencies, numbers formatting, and data standardization software.
  • the DEPCEM is configured to fill in missing data-dictionary entries based on a set of user or administrator defined rules.
  • the DEPCEM is configured to be editable to help ensure that names are properly identified and matched.
  • the system uses the ID iterations to search for an individual’s data and/or records across one or more organizations.
  • ID iterations are automatically associated with the master ID.
  • searching and or entering an ID that matches an ID iteration also returns all data associated with the master ID.
  • the system includes one or more tables for one or more individual types.
  • the system includes a Population Table.
  • the Population Table includes names, records, and/or data elements of people residing and/or who have resided in the United States and/or any foreign country.
  • the Population Table holds over 300 million individual records of the 330 million total records in the United States.
  • the system is configured to be scalable to hold all records worldwide.
  • the Population Table can maintain one or more of the following data for each person: name, name variations (as created by the IMU); addresses, address variations (e.g., with 29 or more or fewer data elements for each address); personal data (e.g., with 20 or more or fewer data elements); retail credit and purchasing preferences (e.g., with 150 data elements); and/or ancillary data obtained from the internet.
  • a Population Table can include and/or link to one or more Variations Tables that includes ID and/or variations for each individual.
  • the system includes an Organizations Table.
  • the Organizations Table holds the names, records, and/or data elements associated with one or more organizations (i.e., agencies). In some embodiments, the Organizations Table can hold over 20 million master records.
  • individuals can be linked to organizations.
  • organizations can include one or more of the following: companies, partnerships, social societies, practices, health plans, groups of individuals, clubs, associations and/or any type or agency.
  • Some embodiments can include any number of data elements (e.g., addresses, telephone numbers, related individuals, organization health plans, related organizations, descriptive codes, services, web links, and/or anything that is associated with an individual the can be described in writing and/or digitally). In some embodiments, there are approximately 60 data elements for organizations. In some embodiments, organizations can be related to other organizations using artificial intelligence and/or statistical analyses techniques.
  • the system includes an Organization Abbreviation Table.
  • organizations i.e., agencies
  • an Organization Abbreviation Table can provide additional name variations.
  • the Organization Abbreviation Table can provide more data points such as multiple addresses, provider affiliations, organizations members, group affiliations, and the like.
  • the Organization Abbreviation Table can be utilized to accurately and precisely identify an organization.
  • Some embodiments of the system comprise an Address Table.
  • the Address Table holds the names, records, and/or data elements associated with one or more Addresses.
  • the Address Table can hold over 135 million master records.
  • one or more Variations Tables are configured to store the variations and/or the source of the variations.
  • one or more Variations Tables include one or more address data elements.
  • Some embodiments include a Dynamic Schema Table.
  • the Dynamic Schema Table can include extensible ahributes.
  • a Data Interface and Management Utility and Report Designer is configured to use one or more Dynamic Schema Table definitions to determine how to format a field in a report.
  • Some embodiments include a Data Interface and Management Utility (DIMU).
  • the DIMU can record field identifiers from any data source and map them to the master identifiers.
  • the DIMU can scan a healthcare data file.
  • the DIMU can ahempt to define the data elements and create a schema (e.g., a Dynamic Schema Table) including field labels.
  • any unresolved field can be resolved manually.
  • the system can remember the edit definitions for the data source and can check for changes or errors in the data for all future loads.
  • the system can automatically build a crosswalk table.
  • the crosswalk table is configured to automatically update changes to data (i.e. the system remembers (i.e., stores, accesses, and retrieves) a client’s administrative system’s tables of providers and member eligibility and can automatically update the client records (and one or more tables) when a change is recorded in the system.
  • the DIMU can include data definition templates.
  • the data definition templates can be included for one or more of: provider networks, claims tables for medical, laboratory, hospital, eligibility, dental and electronic medical records in key systems.
  • one or more templates can be altered and saved as a new template.
  • DMU Data Mastering Utility
  • the DMU can describe any data element.
  • the data element is in one or more databases, tables and/or programs.
  • schema such as field name or variable name, format, data type, size and other database attributes.
  • one or more field labels from any data source can be linked to the system field name.
  • data translation attributes can be specified between a data source and the healthcare data cloud system. In some embodiments, translation attributes allow data conversion to be applied automatically (i.e. meters to feet, kilograms to pounds, and the like, as a non-limiting examples).
  • Some embodiments include a Report Designer and Report Manager Utility (RDMU).
  • the RDMU is configured to allow users to drag-and-drop data elements defined in the DMU.
  • the schema for a data element contains a column width and column label.
  • the columns are distributed across a page and/or automatically formahed for portrait or landscape formats.
  • column totals and averages can be specified for each column.
  • running averages and other computational columns can be inserted along with titles and explanatory text, dates and times.
  • the RDMU can include report templates.
  • the report templates can be used for one or more record of a database (e.g., provider networks, medical facilities, laboratories, hospitals, and dentist).
  • one or more templates are configured to be altered and saved as a new template.
  • the RDMU is configured to schedule report frequency and/or distribution.
  • the system includes a Provider Network Management System (PNMS) in some embodiments.
  • PNMS Provider Network Management System
  • providers are mapped to any number of provider networks.
  • provider networks are defined by state and county or city.
  • subcontracted networks are defined to extend geographic coverage.
  • wrap around networks, out of area networks and specialty networks are defined and linked to a health plan.
  • the PNMS is configured to create networks.
  • the created networks are based on geography, specialty and subspecialty.
  • PNMS is configured to create model networks that are tested against patient distribution and coverage for each patient by specialty.
  • the PNMS is configured to model financial performance when data is available.
  • the PNMS is configured to define a model network.
  • the model network is comprised of any number of contracted PPO networks by using states or counties to specify the geographic area covered by each network.
  • the model network is refined by provider category (i.e. medical, dental, vision, ancillary, and the like) and/or specialty (i.e. primary care, orthopedic, psychiatric, and the like).
  • provider category i.e. medical, dental, vision, ancillary, and the like
  • specialty i.e. primary care, orthopedic, psychiatric, and the like.
  • individual facilities and/or physicians are included or excluded from a model network to meet exact provider network requirements.
  • the PNMS includes a method of use that includes one or more of the following steps: define a model network to meet the requirements and needs of any organization or health plan; find doctors or hospitals contracted by their health plan by using a provider finder web site that accesses the specific provider network assigned to their health plan; automatically transmit a provider network data set to each payer client for use in the claims system of a provider network; access a payer client claims system plan’s specific network through a web service to get up-to-date validation of provider network status; use a web service for a payer client to“identify” the provider in a claim and obtain up-to-date demographic and billing information; and/or use a web service to determine if a provider is in or out of network on a specific date.
  • Some embodiments can include a Provider Specialty Management Module (PSMM).
  • the specialty manager table can incorporate cross-reference technology.
  • the cross-reference technology is configured to automatically create one or more provider types, specialties, and/or subspecialty categories for any client, in any country, and/or in any common languages.
  • cross-reference technology includes the system being configured to use the specialty (and/or specialties) claimed by the healthcare provider and matches the claims to actual diagnoses, procedures and prescriptions issued by the provider and determine if the provider’s practice pahems support the claimed specialties.
  • Some embodiments include a Health Insurance Portability and Accountability Act (HIPPA) compliance module.
  • the HIPAA compliance module is configured to identify a patient, validate the provider’s right to review the patient’s medical records, and/or record that the provider did ahest to the patient signing the HIPAA release.
  • the system is configured to store a copy of the patient release.
  • the HIPPA compliance module is configured to allow a provider to upload documents including the HIPAA release.
  • the system is configured to text the patient’s phone and obtain a text confirmation of the patient’s agreement to release the healthcare records to the provider.
  • Some embodiments of the system include a Data Security and Encryption Module (DSEM).
  • DSEM Data Security and Encryption Module
  • all HIPAA data is retained in separate, secure data areas and related to each of the system modules through encrypted keys.
  • key identifiers are encrypted and retained in secure data areas.
  • user logins are managed through a system identifier that utilizes numerous data elements to identify users and their compliance with HIPAA rules.
  • the system i.e., the Healthcare Data Cloud System; HDC
  • HDC Healthcare Data Cloud System
  • the system provides a web-based service to all healthcare organizations to correlate identifiers, data elements, and/or records associated with an individual a single global HDC identifier. In some embodiments, approximately 300 million people are covered by health plans, but everyone uses the healthcare system and almost all of healthcare are subsidized.
  • FIG. 4 shows many types of healthcare providers and support organizations and all of them maintain their own patient and plan-member identifiers.
  • the system is configured to create a data model for each type of healthcare organization represented in FIG. 4.
  • the healthcare data cloud services market depicted in FIG. 4 is shown as an active web page in the system.
  • each organization type such as individuals, organizations, insurers, HMO’s medical groups, reinsurers, provider networks, etc. require different types of information for operations and decision-making purposes.
  • by clicking on an organization type on the market web page the user defines the data requirements for accepting data from and delivering data to that organization.
  • the organization variations table includes retention models for deriving profitability, utilization, patient mix, high-risk patients and many other factors important to the business of healthcare stored with the organizations profile.
  • the system is configured to manage the data communications between a physician specialty group and an insurance plan administrator just by clicking on the diagram location for both organizations in the active web page depicted in FIG. 4.
  • FIG. 5 illustrates a computer server system network 1830 of the system’s content control server system architecture according to some embodiments of the invention.
  • the computer server system network 1830 comprises a computer server system 1830 configured for operating and processing components of the content control server system architecture 10 in accordance with some embodiments of the invention.
  • the computer system 1830 is configured to process one or more software modules of the aforementioned content control system and method applications and is configured to display information related to user content within one or more graphical user interfaces.
  • the server system 1830 is configured to comprise at least one computing device including at least one processor 1832.
  • the at least one processor 1832 is configured to include a processor residing in or coupled to one or more server platforms.
  • the server system 1830 is configured to include a network interface 1835a and an application interface 1835b coupled to the least one processor 1832 is configured to be capable of processing at least one operating system 1840. Further, in some embodiments, the interfaces 1835a, 1835b coupled to at least one processor 1832 can be configured to process one or more of the software modules (e.g., such as enterprise applications 1838). In some embodiments, the software modules 1838 can include server-based software that is configured to include content control software modules such as a content engine.
  • the software modules 1838 is configured to operate to host at least one user account and/or at least one client account and operate to transfer data between one or more of these accounts using the at least one processor 1832, and process any operation of the content control server system architecture 10 described herein.
  • the invention can employ various computer-implemented operations involving content control data stored in computer systems according to some embodiments.
  • the above-described databases and models throughout the content control can store analytical models and other data on computer-readable storage media within the server system 1830 and on computer-readable storage media coupled to the server system 1830.
  • the above-described applications of the content control system 10 can be stored on computer-readable storage media within the server system 1830 and on computer-readable storage media coupled to the server system 1830. In some embodiments, these operations are those requiring physical manipulation of physical quantities.
  • the server system 1830 is configured to comprise at least one computer readable medium 1836 coupled to at least one data source 1837a, and/or at least one data storage device 1837b, and/or at least one input/output device 1837c.
  • the invention is configured to be embodied as computer readable code on a computer readable medium 1836.
  • the computer readable medium 1836 is configured to be any data storage device that can store data, which can thereafter be read by a computer system (such as the server system 1830).
  • the computer readable medium 1836 is configured to be any physical or material medium that can be used to tangibly store the desired information or data or instructions and which can be accessed by a computer or processor 1832.
  • the computer readable medium 1836 is configured to include hard drives, network attached storage (NAS), read-only memory, random-access memory, FLASH based memory, CD-ROMs, CD-Rs, CD- RWs, DVDs, magnetic tapes, other optical and non-optical data storage devices.
  • various other forms of computer-readable media 1836 is configured to transmit or carry instructions to a computer 1840 and/or at least one user 1831, including a router, private or public network, or other transmission device or channel, both wired and/or wireless.
  • the software modules 1838 is configured to send and receive data from a database (e.g., from a computer readable medium 1836 including data sources 1837a and data storage 1837b that can comprise a database), and data can be received by the software modules 1838 from at least one other source.
  • a database e.g., from a computer readable medium 1836 including data sources 1837a and data storage 1837b that can comprise a database
  • data can be received by the software modules 1838 from at least one other source.
  • at least one of the software modules 1838 is configured to output data to at least one user 1831 via at least one graphical user interface rendered on at least one digital display.
  • the computer readable medium 1836 is configured to be distributed over a conventional computer network via the network interface 1835a where the content control system 10 embodied by the computer readable code can be stored and executed in a distributed fashion.
  • one or more components of the server system 1830 are configured to be coupled to send and/or receive data through a local area network (“LAN”) 1839a and/or an Internet coupled network 1839b (e.g., such as a wireless Internet).
  • LAN local area network
  • an Internet coupled network 1839b e.g., such as a wireless Internet
  • the networks 1839a, 1839b are configured to include wide area networks (“WAN”), direct connections (e.g., through a universal serial bus port), or other forms of computer-readable media 1836, and/or any combination thereof.
  • WAN wide area networks
  • direct connections e.g., through a universal serial bus port
  • other forms of computer-readable media 1836 and/or any combination thereof.
  • components of the networks 1839a, 1839b are configured to include any number of user devices such as personal computers including for example desktop computers, and/or laptop computers, or any fixed, generally non-mobile Internet appliances coupled through the LAN 1839a.
  • some embodiments include personal computers 1840a coupled through the LAN 1839a that are configured for any type of user including an administrator.
  • Some embodiments include personal computers coupled through network 1839b.
  • one or more components of the server system 1830 are configured to send or receive data through an Internet network (e.g., such as network 1839b).
  • some embodiments include at least one user 1831 coupled wirelessly and accessing one or more software modules of the content control system 10 including at least one enterprise application 1838 via an input and output (“I/O”) device 1837c.
  • the server system 1830 can enable at least one user 1831 to be coupled to access enterprise applications 1838 via an I/O device 1837c through LAN 1839a.
  • the user 1831 is configured to comprise a user 1831a coupled to the server system 1830 using a desktop computer, and/or laptop computers, or any fixed, generally non- mobile Internet appliances coupled through the Internet 1839b.
  • the user 1831 can comprise a mobile user 1831b coupled to the server system 1830.
  • the user 1831b can use any mobile computing device 1831c to wireless coupled to the server system 1830, including, but not limited to, personal digital assistants, and/or cellular phones, mobile phones, or smart phones, and/or pagers, and/or digital tablets, and/or fixed or mobile Internet appliances.
  • any mobile computing device 1831c to wireless coupled to the server system 1830, including, but not limited to, personal digital assistants, and/or cellular phones, mobile phones, or smart phones, and/or pagers, and/or digital tablets, and/or fixed or mobile Internet appliances.
  • the server system 1830 is configured to enable one or more users 1831 coupled to receive, analyze, input, modify, create and send data to and from the server system 1830, including to and from one or more enterprise applications 1838 running on the server system 1830.
  • at least one software application 1838 running on one or more processors 1832 is configured to be coupled for communication over networks 1839a, 1839b through the Internet 1839b.
  • one or more wired or wirelessly coupled components of the network 1839a, 1839b is configured to include one or more resources for data storage.
  • this can include any other form of computer readable media in addition to the computer readable media 1836 for storing information, and can include any form of computer readable media for communicating information from one electronic device to another electronic device.
  • FIG. 6 illustrates another flow chart of the system’s operations and components according to some embodiments.
  • the system includes a Data Interference and Management Utility; a Data Mastering Utility; an Identification Mastering Utility; External Data collected from one or more external databases; Identification Logic; an Individual Name Variations Table; and Organization Name Variations Table; an Identifiers Table; and Address Variations Table; and/or a Natural Language Variations table.
  • the arrows represent a bi-directional flow of data between each utility.
  • Applicant defines the use of and/or, in terms of“A and/or B,” to mean one option could be“A and B” and another option could be“A or B.” Such an interpretation is consistent with ex parte Gross, where the Board established that“and/or” means element A alone, element B alone, or elements A and B together.
  • the invention also relates to a device or an apparatus for performing these operations.
  • the apparatus can be specially constructed for the required purpose, such as a special purpose computer.
  • the computer can also perform other processing, program execution or routines that are not part of the special purpose, while still being capable of operating for the special purpose.
  • the operations can be processed by a general-purpose computer selectively activated or configured by one or more computer programs stored in the computer memory, cache, or obtained over a network.
  • data can be obtained over a network the data can be processed by other computers on the network, e.g. a cloud of computing resources.
  • Some embodiments of the present invention can be defined as a machine that transforms data from one state to another state.
  • the data can represent an article, that can be represented as an electronic signal and electronically manipulate data.
  • the transformed data can, in some cases, be visually depicted on a display, representing the physical object that results from the transformation of data.
  • the transformed data can be saved to storage generally or in particular formats that enable the construction or depiction of a physical and tangible object.
  • the manipulation can be performed by a processor. ', the processor thus transforms the data from one thing to another.
  • the methods can be processed by one or more machines or processors that can be connected over a network.
  • each machine can transform data from one state or thing to another, and can also process data, save data to storage, transmit data over a network, display the result, or communicate the result to another machine.
  • computer readable storage media refers to physical or tangible storage (as opposed to signals) and includes without limitation volatile and non-volatile, removable and non-removable storage media implemented in any method or technology for the tangible storage of information such as computer-readable instructions, data structures, program modules or other data.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Theoretical Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Dans certains modes de réalisation de la présente invention, un système intègre différents identifiants uniques provenant d'organismes et d'organisations et associe les différents identifiants uniques les uns aux autres dans une table. Dans certains modes de réalisation, la table relie tous les différents identifiants uniques à un seul identifiant de sorte qu'une recherche d'un identifiant renvoie des liens à tous les enregistrements et à toutes données associé(e)s à l'individu. Dans certains modes de réalisation, le système collecte les différents identifiants uniques provenant d'organisations telles que des services médicaux de patient, des bases de données d'application de lois locales et nationales, et des enregistrements de sociétés privées. Dans certains modes de réalisation, le système analyse chaque composant de chaque identifiant et les stocke en tant que variation. Dans certains modes de réalisation, chaque identifiant analysé est associé à un identifiant maître. Dans certains modes de réalisation, le système relie l'identifiant maître à tous les enregistrements et à toutes les données parmi de multiples organisations et organismes.
PCT/US2020/028274 2019-05-14 2020-04-15 Système infonuagique de données de soins de santé, serveur et procédé WO2020231590A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201962847469P 2019-05-14 2019-05-14
US62/847,469 2019-05-14

Publications (1)

Publication Number Publication Date
WO2020231590A1 true WO2020231590A1 (fr) 2020-11-19

Family

ID=73288801

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2020/028274 WO2020231590A1 (fr) 2019-05-14 2020-04-15 Système infonuagique de données de soins de santé, serveur et procédé

Country Status (2)

Country Link
US (1) US12051502B2 (fr)
WO (1) WO2020231590A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11410658B1 (en) * 2019-10-29 2022-08-09 Dialpad, Inc. Maintainable and scalable pipeline for automatic speech recognition language modeling
US11861037B2 (en) * 2019-11-04 2024-01-02 Aetna Inc. Unified data fabric for managing data lifecycles and data flows

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070299697A1 (en) * 2004-10-12 2007-12-27 Friedlander Robert R Methods for Associating Records in Healthcare Databases with Individuals
US20090024589A1 (en) * 2007-07-20 2009-01-22 Manish Sood Methods and systems for accessing data
US20120102002A1 (en) * 2010-10-25 2012-04-26 Vinaya Sathyanarayana Automatic data validation and correction
KR20160059294A (ko) * 2014-11-18 2016-05-26 삼성생명보험주식회사 고객 컨설팅을 위한 컨설팅 정보 생성 방법, 장치 및 컴퓨터 프로그램
US20160154859A1 (en) * 2003-05-29 2016-06-02 Experian Marketing Solutions, Inc. System, Method and Software for Providing Persistent Entity Identification and Linking Entity Information in a Data Repository

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100169348A1 (en) * 2008-12-31 2010-07-01 Evrichart, Inc. Systems and Methods for Handling Multiple Records
JP6417727B2 (ja) * 2014-06-09 2018-11-07 富士通株式会社 情報集約システム、プログラム、および方法
JP6582723B2 (ja) * 2015-08-19 2019-10-02 富士通株式会社 ネットワークシステム、スイッチ装置、及びネットワークシステム制御方法
EA201990931A1 (ru) * 2016-10-11 2019-11-29 Способ и система для передачи данных биоинформатики
US10671625B2 (en) * 2017-01-26 2020-06-02 Sap Se Processing a query primitive call on a value identifier set

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160154859A1 (en) * 2003-05-29 2016-06-02 Experian Marketing Solutions, Inc. System, Method and Software for Providing Persistent Entity Identification and Linking Entity Information in a Data Repository
US20070299697A1 (en) * 2004-10-12 2007-12-27 Friedlander Robert R Methods for Associating Records in Healthcare Databases with Individuals
US20090024589A1 (en) * 2007-07-20 2009-01-22 Manish Sood Methods and systems for accessing data
US20120102002A1 (en) * 2010-10-25 2012-04-26 Vinaya Sathyanarayana Automatic data validation and correction
KR20160059294A (ko) * 2014-11-18 2016-05-26 삼성생명보험주식회사 고객 컨설팅을 위한 컨설팅 정보 생성 방법, 장치 및 컴퓨터 프로그램

Also Published As

Publication number Publication date
US12051502B2 (en) 2024-07-30
US20210043319A1 (en) 2021-02-11

Similar Documents

Publication Publication Date Title
US8527292B1 (en) Medical data analysis service
US7917378B2 (en) System for processing healthcare claim data
DiGaetano Sample frame and related sample design issues for surveys of physicians and physician practices
US20030191669A1 (en) System for providing consumer access to healthcare related information
US20160034578A1 (en) Querying medical claims data
CN109542966B (zh) 数据融合方法、装置、电子设备及计算机可读介质
US20160321412A1 (en) Cost, Quality and Distance Based Method and System for Health Care Referrals
US7464043B1 (en) Computerized method and system for obtaining, storing and accessing medical records
US20210174380A1 (en) Efficient data processing to identify information and reformant data files, and applications thereof
US12051502B2 (en) Healthcare data cloud system, server and method
Canterberry et al. The patient-centered outcomes research network antibiotics and childhood growth study: implementing patient data linkage
Ai et al. A systematic review and qualitative assessment of fraud detection methodologies in health care
Haeder et al. Mixed signals: The inadequacy of provider‐per‐enrollee ratios for assessing network adequacy in California (and elsewhere)
Dygaszewicz Transition from traditional census to combined and registers based census
Katurura et al. A review of the implementation of electronic health record systems on the African continent
Dixon et al. Health information exchange and Interoperability
US20230394233A1 (en) System, server and method for training artificial intelligence using vectorized data
Tomashchuk et al. Operationalization of privacy and security requirements for eHealth IoT applications in the context of GDPR and CSL
Yazla et al. Evolution of Telepsychiatry: Scientometric Analysis of Telepsychiatry Publications Between 1986 and 2019
Rahimisadegh et al. Network analysis of Iranian's health insurance ecosystem before and after the introduction of Universal Health Insurance law
US20190115100A1 (en) Systems and Methods of Processing and Reconciling Healthcare Related Claims
Sampurno et al. Development of technologic solutions to address complex local requirements of an International Prostate Cancer Clinical Quality Registry
KR102549638B1 (ko) 비급여 데이터를 분류하는 서비스를 제공하는 방법, 장치, 및 시스템
US20090171854A1 (en) Increasing health insurance benefits
Langley et al. Blockchains, property rights and health technology assessment in the pharmaceutical and device (s) industries

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20806360

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20806360

Country of ref document: EP

Kind code of ref document: A1